Imaging applications must always address the problem that the world is multidimensional. But, most detectors (e.g., focal plane arrays) are essentially two dimensional recording devices. Capturing an image on a single frame requires the projection of the actual scene onto this detector, and the multidimensional information is inaccessible. However, by taking several projections in two dimensions, one can reconstruct some of the additional information.
This effect is particularly relevant to hyperspectral imaging, detection, and cueing. Hyperspectral data is typically described as a three dimensional data cube, with two spatial dimensions and one spectral dimension. When an imaging spectrometer samples an object, it records the unique spectrum at each sampled pixel location on the object. Since most focal plane arrays are two dimensional, this three dimensional data cube must be projected onto two dimensions, and reconstructed over several frames.
There are many ways to accomplish this projection, and each has advantages and disadvantages depending on the intended application. Traditional airborne or space based hyperspectral imagers typically use a narrow slit to admit only a line image to the spectrometer. A grating or prism then disperses this light in the direction perpendicular to the slit. In this way, the spectrum for each pixel in the line image is then recorded. The natural motion of the aircraft or spacecraft is then used to advance the spectrometer in space, and once this has occurred, a new image is recorded on the detector. The three dimensional image is then built up over time, line by line, as the platform moves across the scene. When collected in this way, the spectrum for each pixel in a line is recorded at the same time, and so it is easy to match a particular spectrum to a particular point in object space. However, the line image restriction requires scanning, and makes real-time processing based on two dimensional images that require multiple looks of the same scene (such as tracking applications) difficult. This type of system is generally referred to as a “pushbroom” type sensor, as the data is collected by scanning the slit over object space in the same way a broom pushed to clean a floor.
A different approach is to use a filter wheel or liquid crystal variable filter, and to take successive two dimensional images with different filters in place. In this approach, the hyperspectral data cube is also assembled over time. This type of system is useful for ground-based applications wherein the sensor is stationary; a moving platform will cause spectral mis-registration as the spectrum for each pixel is collected over time. Moving objects in the scene will cause mis-registration as well. This approach is useful for applications that require real-time processing based on multi-look two-dimensional images, but the mis-registration issues can sometimes lead to complicated correction efforts, especially for high speed platforms or fast moving objects. This type of system is generally referred to as a “staring” type sensor, as it can obtain a two dimensional image without scanning the scene.
Approaches also exist to capture the complete data cube on a single focal plane array. One example is a compressive imaging scheme that collects fewer data points than are contained in the entire cube (e.g., allowing the 3-D cube to be imaged on a 2-D focal plane array). But, the resultant output is an estimation of the true data cube based on a reconstruction algorithm. An alternative snapshot design, the Computed Tomography Imaging Spectrometer (CTIS), obtains the data cube by making many different projections over the entire array, resulting in a very low resolution data cube compared to the site of the focal plane array employed.